Health Datapalooza IV: June 3rd-4th, 2013
Uses of CMS Data in Rapid-Cycle Innovation
Moderator:
Kavita Patel, Managing Director for Clinical Transformation and Delivery, Brookings Institution
Speakers:
Rocco Perla, Director, Learning and Diffusion Group, Centers for Medicare & Medicaid Services
Hoangmai Pham, Director, Division of Accountable Care Organization Populations, Centers for Medicare & Medicaid Services
Will Shrank, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard Medical School
Farzad Mostashari, National Coordinator for Health Information Technology (ONC), US Department of Health and Human Services
The rapid proliferation of health data and improved usability has led to a sea change in how new health programs are designed, implemented and evaluated and in the speed in which innovation can occur. In this session, officials from the Centers for Medicare & Medicaid Services and the Office of the National Coordinator for Health Information Technology will describe specific government programs or functions that rely on rapid use of data to support patient targeting, feedback and learning, and highlight developments in data use that will promote innovation to deliver higher quality care at lower costs to patients.
This session is eligible for continuing education credit.
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Health Datapalooza 2013: Uses of CMS Data in Rapid-Cycle Innovation - Rocco Perla & Hoangmai Pham
1.
2. Type Purpose Characteristics
Formative Local improvement, rapid
testing, inform next
test, shape outcomes
Smaller scale, small
n’s, messy, unadjusted, t
argeted samples, time
series
Summative Large scale
benefit, determine net
effects, summarize
outcomes
Larger scale, large
n’s, power, adjusted, one
large test, p-values
5. Data Tool From IOM Website
Goal: explore current distribution of FFS spend surfacing ideas re how to impact costs.
6. UCL 10414.15
CL 7994.21
LCL 5574.27
4487.15
5487.15
6487.15
7487.15
8487.15
9487.15
10487.15
11487.15
AK AR CA CT DE GA IA IL KS LA MD MI MO MT ND NH NM NY OK PA RI SD TX VA WA WV
StandardizedRisk-AdjustedPerCapitaCosts^
State
X Chart (Standardized Risk-Adjusted Per Capita Costs - 2010)
BA
7. Data Tool From IOM Website
Drill Down
3000
3500
4000
4500
5000
5500
6000
6500
7000
7500
8000
8500
2007 2008 2009 2010
Standardized Risk-Adjusted Per Capita Costs – Over Time
National Per Capita Costs State A Per Capita Cost State B Per Capita Cost
8. But, at some point as you
increase the amount of
information you actually
begin to increase the level
of confusion again.
Confusion
InformationLow High
High
There is a point at which
increasing information
reduces confusion.
15. Pioneer ACO Driver Diagram
June 2012
100% of the original
Pioneer ACOs
generate sufficient
cost savings and
quality
improvements to
qualify for
population-based
payments in year
three of the Model
ACO
demonstrates
significant cost
savings and
quality
improvements
CMMI
demonstrates
an effective ACO
model that can
scale
Aim
• Peer based learning
• Strong data systems
• Staff and resources for continuous improvement
• Capacity to test new ideas at point of care
• Population-based measurement
• New payment models for clinicians
• Clinician involvement in changes
• Involvement of patients/consumer advocates in
change
• New and innovative communication strategies
• Engagement with private payers and Medicaid
agencies
• ACO models understood by state insurance
commissioners and other regulatory bodies
• R&D, innovation, and breakthrough ideas
• Adaptation of the ACO framework at a regional and
local level
• Adaptation at a national and regulatory level
Continuous care
improvement driven by data
Utilization patterns for value
based care
Organizational structure
capable of achieving results
Productive relationships with
providers
Beneficiary engagement
Continuous evolution of the
ACO model
Trustworthy partnership
and unconditional teamwork
with CMS
Primary Drivers Secondary Drivers
Payment reform
(with risk for gains or losses)
• Recognition of Pioneers as national leaders
• Effective communication
• Effective and collaborative problem-solving
• CMS facilitation of and technical assistance for
peer-based learning
• CMS and Pioneers honor the Pioneer Agreement
• Coordinated care transitions
• Care management of high risk patients
• Utilization of primary care services
• Appropriate use of hospital and ancillary services
• Others - TBD by Pioneers
• Effective leadership and governance
• Improvements in overhead and waste
(i.e., non-value added activities)
16. The distributions of six
utilization measures (out of
20) are shown. The top 5% of
the non-annualized
expenditure (upper
left, Q3, median 40.2%
)demonstrate higher than the
Reference Population(RP)
(median 39.8%); while
hospitalization
events(lower, right, Q3, medi
an 370.2) are fewer than the
RP(Q3, median 382.3). The
changes over the three
quarters vary among
measures, but most changes
are less than one percent and
they are also not statistically
significant.
19. 19 Pioneers increased provider participation
between PY1 and PY2
5 Pioneers had little change (-15 and +15)
8 Pioneers decreased Provider Participation
Of the models that we are supporting, there are No “Turnkey” Solutions – providers cannot simply flip a switch and optimally implement an ACO model, transform primary care practices, or coordinate all services in a hospitalThe models we will require fundamental changes in the structure of healthcare delivery, as we realign incentives for health systems, primary care, hospitals and other health servicesIn each of these cases, Substantial learning and adaptation will be necessary before achieving the greatest efficienciesHealthcare delivery in these models will be maturing once implemented – they wll not be staticIt is important to note that while we all agree that RCTs offer the most incontrovertible evidence about the effect of an intervention, they will not be feasible in most cases for the Innovation Center. Practically, we must identify providers who are willing and eager to participate in the models we plan to test. This practicality, along with our need to move quickly, require that we implement “natural experiments” and use quasi-experimental designs to evaluate effectiveness.